Study on surface enhanced Raman spectroscopy of kidney cancer based on Ag NC enhanced substrate

Surface Enhanced Raman Spectroscopy (SERS) has been widely used as a sensitive sensing technology in the medical field because of its unique molecular fingerprint information. In this paper, silver nanospheres (Ag NPs) and silver nano-cube (Ag NC) nanoparticles with different morphologies were constructed based on silver nanomaterials for the purpose of early screening of kidney cancer. By analyzing the SERS characteristics of nanoparticles, it was found that the enhancement effect of Ag NC was greater than that of Ag NPs. SERS detection was performed on the urine of 30 patients with kidney cancer and 30 normal subjects. By analyzing the spectral differences between cancer patients and normal people, the cancer group and normal people were preliminarily distinguished. We analyzed the measured spectral data. The analysis methods mainly included Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) diagnostic algorithms, as well as recursive weighted Partial Least Squares (PLS) and Support Vector Machine (SVM) algorithms. The comparison of the two classification algorithms shows that the classification accuracy of PLS-SVM is 99.13%, sensitivity was 98.67%, specificity was 100%, and AUC value was 1. The classification effect was much higher than PCA-LDA. The results of this Exploratory research show that the combination of Ag NC substrate and PLS-SVM algorithm has greater potential in the pre diagnosis and screening of Kidney cancer.